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■ Institute Overview—2■ Research Centers ——————————————

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NOE ( Network Of Excellence ) Project
Project for Fostering and Promoting
Statistical Thinking

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■ History—

Message from Director-General

This is the last fiscal year that I will serve as the Director-General. I took overthe post from the former Director-General, Dr. Kitagawa, in April 2011, just after
the Great Eastern Japan Earthquake. Immediately after I took office, the “Big
Data” era arrived, and the media began to focus on the training of people who
can make decisions based on data, a cause for which the Institute has foughtalone for a long time at the national level. In addition, due to the AI (Artificial Intelligence) boom for the last two or three years, statistics and mathematical science, which constitute the foundation of AI technology, have been fervently spotlighted. In the present moment, statistical mathematics benefits from the highest
degree of understanding by the public over the long history of our institute,which will celebrate its 75 th anniversary next year. Under these circumstances,
I always felt very responsible during my seven-year term as the Director-General,
but at the same time I had the opportunity to perform highly challenging work.
Due to the financial situation, which has gotten harsher every year, it was difficult to say whether I could sufficiently meet expectations from people outside
the organization. However, thanks to support from the research communities and the sincere cooperation of thestaff of the Institute, I believe that I have adequately achieved the goals that I had when I took this post.
While I have only one year remaining in my term, we have embarked on the reform of our two main projects, theNOE (Network Of Excellence) Project and the Project for Fostering and Promoting Statistical Thinking. At the end of
the fiscal year before last, we closed two NOE-type research centers, and last July we launched the Data Science
Center for Creative Design and Manufacturing. Moreover, this April we set up the new Research Center for Medicaland Health Data Science, with the goal of establishing structures for statistical education and research support in
the national medical academia and to promote the upgrading of data science research. As I explained above, theNOE projects offer the opportunity to promote new transdisciplinary research projects while responding to current
social needs by boldly restructuring the groups of our research centers.
As for the Project for Fostering and Promoting Statistical Thinking, in the last fiscal year we launched the Leading
DAT (Data Analysis Talents) program, dedicated to fostering world-class data scientists, by fundamentally revising
our long-renowned tutorial courses. Among the Leading DAT program, the Leading DAT Training Course is ourunique attempt to conduct intensive training for a small number of people with high potential. The training consistsof practical problem-solving exercises and special talks, in addition to the contents of the Leading DAT lectures.
Participants who successfully complete the course will be granted a certificate of completion. In the last fiscal year
the first group of more than 20 participants finished the course.
At present, in both public and private universities, a wide variety of reforms are underway to allow students tostudy data science systematically from the undergraduate to the Master’s level. Under these circumstances, the Institute intends to strengthen various programs targeted at postdoctoral fellows and young faculty members, with afocus on recurrent education of working technicians (researchers). We are trying to respond to the critical shortageof data scientists and AI technicians by fostering people who can ensure the implementation of data science education with proper timing.
It is essential for the institute to globalize statistical mathematics. These days, we are placing more emphasis on
the enrichment of programs conducted according to the academic exchange agreements than on increasing thenumber of such agreements. In fact, mutual exchange of postdoctoral fellows and young faculty members, as well as
joint research based on such programs, has become very active. In particular, we see the dynamic expansion of
exchange with Asian countries, significantly promoted by support from URAs (University Research Administrators). In addition to promoting exchange with foreign institutions, we have made efforts to reinforce the functions ofstatistical mathematics, which has the effect of linking various fields and people through enhancing the diversity offaculty. To this end, we take pro-active measures such as preferentially employing young people after the retirementof senior faculty members or their departure to outside institutions, as well as actively employing women and foreigners. Consequently, the Institute is now filled with a youthful atmosphere, and we anticipate that this fresh sensibility will lead to exciting research projects. In the one year that is left to me as Director-General, I intend to contribute even more intensively to the Institute, as well as to society at large. I would appreciate your continued support.

Tomoyuki Higuchi
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on structural modeling of physical phenomena related to numerous factors,
and also conducts research on model-based statistical inference methodologies. By means of model-based prediction andcontrol, modeling of complex systems, and data assimilation, the department aims to contribute to the development ofmodeling intelligence in many fields.

■ Prediction and Control Group ————————————————————————————————————————————The Prediction and Control Group works on the development and evaluation of statistical models, which function effectively
in terms of prediction and control of phenomena, decision making, and scientific discoveries. These efforts involve data
analysis and modeling related to phenomena that vary across time and space.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies aimed at discovering the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling. For these purposes, the group also considers
Monte Carlo simulations, discrete mathematics, and software development.

■ Data Assimilation Group ———————————————————————————————————————————————The Data Assimilation Group works on the development of data assimilation techniques, which are procedures aimed
at combining information derived from large amounts of observations and a numerical simulation model. By developing
computational algorithms and high-performance parallel computing systems, the group aims to build a next-generation
simulation model that can predict the future in real time.

Department of Statistical Data Science
The Department of Statistical Data Science conducts research on data design methods aimed at managing uncertainty
and incompleteness of information, quantitative methods for evidence-based practice, and related data analysis methods.
Moreover, the department investigates methods for inferring the latent structures in target phenomena from observation data.

■ Survey Science Group ————————————————————————————————————————————————The Survey Science Group promotes research on the design of statistical surveys, development of statistical analysis
methods on survey data, and applications. By exploring complex phenomena in various fields, the group also aims tocontribute to practical applications in academia and policy-making through social surveys.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group conducts research aimed at identifying and evaluating statistical evidence through quan­tification of phenomena that have not been measured thus far, as well as efficient information extraction from large
databases. The group investigates related methods and develops methods for analyzing the collected data. By working
on applied research in various fields of real science, the group aims to advance practical, applied, statistical mathematicalresearch based on evidence.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group conducts research on statistical science aimed at inferring the latent “structure” behindvarious target phenomena in biology, physics, and social science, based on observational data. The group focuseson machine learning, Bayesian reasoning, experimental design methods, and spatial-temporal analysis methods toinvestigate micro/meso/macroscopic and spatial-temporal dynamic structures in target phenomena.

Department of Statistical Inference and Mathematics
The Department of Statistical Inference and Mathematics carries out research into general statistical theory, statisticallearning theory, optimization, and algorithms for statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain
phenomena, stochastic processes and their application to inference, probability and distribution theory, and the related
mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data math­ematically and clarify the potential and the limitations of the data theoretically.

■ Mathematical Optimization Group —————————————————————————————————————————The Mathematical Optimization Group focuses on mathematical theory and practical applications of optimization andcomputational algorithms together with underlying numerical or functional analysis and discrete mathematics.

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NOE-type Research
Risk Analysis Research Center
Risk Analysis Research Center is pursuing a scientific approach to managing uncertainties and risks in society, which haveincreased with the growing globalization of society and economy. Our research projects are mainly seismology, finance,
resources, environmentology, database development and risk mathematics. The Center also manages associated with a net­
work organization for risk analysis with the goal of contributing to creating a safe and resilient society.

Research and Development Center for Data Assimilation
Research and Development Center for Data Assimilation aims to construct simulation models that can predict the future
and to design effective observation systems by means of “data assimilation”, which is a fundamental technology integrating
numerical simulations and observational data.

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data. This field
is based on both the statistical science concerning inference from data and computer science concerning efficient algorithms.
It can be applied to broad disciplines ranging engineering and information science to natural science, such as robotics andbrain sciences. Our research center aims at supporting the academic community of this field, as well as producing influential
research through various joint projects.

Data Science Center for Creative Design and Manufacturing
We aim to foster new scientific methods for innovative design and manufacturing. Various fields in manufacturing are now
facing a revolutionary period. Population reduction and globalization are bringing dramatic changes in the industrial structure
in Japan. Countries around the world has actively developed their growth strategies utilizing data science as a driving force.
We have accumulated state-of-the-art technologies in data science here. We are devoted to foster and practice advanced
methods in data science for design and manufacturing.

Research Center for Medical and Health Data Science
Research Center for Medical and Health Data Science aims to facilitate statistical data science research that covers medical
studies, drug developments, health care, and public health. Our research projects involve fundamental mathematics and compu­
tational science for medical applications, applied methodology for basic, clinical and social medicine, and modern technology
such as artificial intelligence, machine learning, and big data analyses. Furthermore, our research center aims at constructing
a research network of the academic community of this field, as well as offering advanced statistical education programs.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking, fromextension courses to a professional development program. The researchers affiliated with the school are often involved withspecific data analysis projects, which help them to gain hands-on knowledge of data science. We expect such an experienced
researcher will play an active role as a number-cruncher, as a modeler, or as a project coordinator.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways: managing
computer systems and networks, editing and publishing journals, maintaining the library, and manage extension courses.

■ Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities and scientific software.
■ Computer Networking Unit The Computer Networking Unit is responsible for computer networking and its infrastructure,and network security.

■ Information Resources Unit The Information Resources Unit is responsible for maintaining a library and an electronic
repository, and is in charge of planning statistical education courses open to the public.

■ Media Development Unit The Media Development Unit is in charge of publishing and editing of research results andPR brochures.

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Research Centers
Risk Analysis Research Center

Statistical Disclosure Control for Tabular Data
N On-site service for accessing official microdata
For many years, the Japanese government has been
conducting various surveys (e.g., the Population Census) and has recently launched the on-site use service
that enables a researcher to access official microdata
at a secure on-site facility, as shown in Figure 1.
However, since official microdata might contain sensitive information on participants of the surveys, a researcher can receive only analysis results that satisfy
safety requirements examined by an output checker.
To pass the output checking process, it is necessary to
perform statistical disclosure control on analysis results,
which partially masks the original information.

Figure 1: On-site use service in Japan.

N Sensitive cells in tabular data
Tabular data is a base for the analysis of official microdata. However, there exist significant risks of revealing sensitive information in microdata through table cells of small unit counts.
Consider a frequency and a magnitude tables of two
variables, Jobs and Regions, in Figure 2. Since there exists only a single unit in cell (M2 , P3 ), the corresponding cell of the magnitude table reveals the exact income (i.e., 22) of that unit. Also, even if the cell (M 3, P5 )contains two units, either one of the two units contributing to the cell can obtain the income of the other unit
by subtracting his own salary from the sum of the twoin the magnitude table.
We, therefore, consider cells of small counts sensitive and suppress those small cell values. However,since it is possible to restore suppressed cell values
by considering linear relationships among cell variables in terms of marginal sums, we need to perform

Figure 3: Statistical disclosure control tool for tabular data in R.

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Figure 2: Disclosure risks in sensitive tabular cells.

additional secondary cell suppressions to resolve thisissue.
N Statistical disclosure tool in R
To determine a set of secondary suppressed cells
requires solving an optimization problem of minimizing information loss under the constraints of ensuring
enough uncertainty on the values of primary
suppressed cells. Since it is infeasible to manually perform this task, we have been developinga statistical disclosure control tool in R.
Our tool, which consists of a set of R functions, supports various sensitivity rules, such as(n, k)-rule, p% rule, for primary suppressions
and performs secondary suppressions while
producing auxiliary tables that prove the safety of the suppressed table. For future work, weplan to evaluate the usability and performanceof our tool with researchers who use the on-site
service in Japan.
Kazuhiro Minami

Research Consortium for Asian Agro-Forest Resource Management“A2gFReM Research Consortium”
N Resource management

Among Asian countries, ecosystems like croplands, forests, lakes and rivers are considered important natural resources because of the various ecosystem services they provide, such as biodiversity
and carbon sequestration. They also make significant contributions to the socioeconomic development of these countries through economic production. Therefore, any mismanagement or misuse of
these resources can threaten the basis of their livelihood. For example, without appropriate spatial andtemporal planning for expansion of palm tree plantation to meet the needs of rising domestic and international demand for palm oil, can lead to degradation of forest ecosystems and increase the risk ofdeteriorating ecosystem services, which can negatively impact local communities. Also, the mismanagement or misuse of croplands adjacent to rivers
and lakes can negatively impact those ecosystems interms of water quality and aquatic life. If these negative impacts are quantified by the best scientific evidence and latest statistical techniques, we can seek
an optimal solution considering the negative impacts. From these results, policies can be framed tomitigate the risk of losing ecosystem services and ensure sustainable economic development that meetsthe need of local communities.
The objective of this project is todevelop a research consortium (called
A 2gFReM Research Consortium) to
conduct a collaborative research on
agriculture and forest resource management in Asian countries such as
Cambodia, Vietnam, Nepal, Laos andIndonesia (Fig 1). In the resource management, it has been the desire to collect and archive field data, develop adata base, conduct statistical analyses, project the availability of natural
resources in the future, and develop adecision support system for managing
their natural resource efficiently andeffectively using a mathematical programing approach. The system described above eventually help us to
conduct policy analyses based on best

Workshop in Cambodia, 2017

scientific evidence available and the latest analytical
tools. The research consortium also provides an opportunity to conduct collaborative researches, andfinally but not the least, contribute to capacity building of young scholars and practitioners in Asian countries through the workshop.
N Research collaboration through MOU:
http://formath.jp/workshop/

We have already signed MOUs and MOA with governmental research institutes in Cambodia and Vietnam and national universities in Nepal and Laos. We
are currently in the process of signing another MOU
with a national university in Indonesia.
Atsushi Yoshimoto

Figure 1: Overview of A 2gFReM Research Consortium.

5Research and Development Center for Data Assimilation

Data Assimilation for Estimating Magnetospheric Environment
The magnetosphere, which is the outer space region from thousands to hundred thousands km in altitude, is important for human activities because alarge number of artificial satellites such as navigation satellites and weather satellites are operated as
infrastructures. In the magnetosphere, charged particles are widely distributed. Those charged particles may cause various phenomena which affect human activity. For example, spacecraft charging due
to high-energy charged particles can destroy electronic devices installed on the spacecraft. Aurora
storm due to dynamical process of charged particles
in the magnetosphere can interfere with electromagnetic communications which may affect aircraft
operations at high latitudes. It is thus demanded tomonitor the state of the spatial distribution and temporal variation of charged particles in the magnetosphere.
Although in-situ observations by artificial satellites are widely used for monitoring the state of themagnetosphere, it can not provide the global state of
the magnetosphere because each individual satellite
provides only the information at its location. However, useful tools for obtaining the global state of themagnetosphere have recently become available. One
is imaging observations which remotely obtain global images about the magnetosphere. The informationof the low-energy charged particle distribution canbe obtained with an extreme ultraviolet (EUV) camera, and the information on the high-energy charged
particle distribution can be obtained with the remote observation of energetic neutral atoms (ENA)
generated from high-energy ions and low-energy
neutral particles. The other useful tool is networks ofground-based observations. Since various physicalphenomena would propagate along terrestrial magnetic field lines down to the ionosphere below 1000km altitude, ground-based observations of the ionosphere can obtain various signals from the magnetosphere.
N Assimilation of remote observation data

These remote observations do not provide direct
information on meaningful physical variables in themagnetosphere. Our research group therefore aims
at enabling us to estimate the global structures andtemporal variations of physical variables in the magnetosphere by using the data assimilation approach.
We are developing a data assimilation system which
incorporates the EUV and ENA data obtained fromthe IMAGE satellite operated by NASA. The result of
the experiment with artificial data sets of EUV and
ENA demonstrates how well our data assimilation
system works.
Shin’ya Nakano

Figure 1: Schematic picture of remote observationfrom the IMAGE satellite.

Figure 2: Result of the data assimilation experiment with artificial ENA andEUV data.

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Research Centers

Flood Risk Trade-Off Analysis Using High-Resolution RemotelySensed Imagery
N Flood risk and trade-off

Along with the progress of global warming, adaptation and mitigation of cities to climate change attracts a considerable attention. Especially, in Japan,
where storm and flood disaster risks are projected toincrease, it is an emergent task to build resilienceagainst these risks in bayside cities. On the other
hand, bayside areas are also attractive districts inmany cases, with scenic ocean view and rich naturalenvironment. It is important to make disaster prevention policies considering the trade-off between
disaster risks and positive benefits from the ocean.
N High-resolution remote sensing data and urban

monitoring
Recent remote sensing (RS) technology enables
us monitoring urban environment in a high-resolution manner. For example, Digital Surface Model(DSM; Figure 1, 2) is a digital representation of the
3-dimensional urban space, which was created using
observations acquired by LiDAR (Light Detection

Figure 1: Digital Surface Model ( DSM; Center of Yokohama ).

And Ranging), which is an RS observatory. In thisstudy, the DSM was used to evaluate (a) flood riskand (b) positive benefits from the ocean, including
openness of view, goodness of ocean view, distanceto the ocean, and greenness of the neighborhood.
N Quantification of flood risk trade-offs

This study analyzes the trade-off between (a)flood risk and (b) positive benefits from the ocean by
estimating their non-linear impacts on condominium
unit prices. Figure 3 shows estimated impacts from(b) open view, ocean view, and (a) flood risk. This
figure suggests that open/ocean view have positive
impact on residential prices. (Although not shown in
the figure,) it is also suggested that bayside neighborhoods with much green areas have greater value
than the other districts. These results are intuitively
reasonable. By contrast, any statistically significant
impacts are not found from flood risk. Based in the
result, flood risks might have been underestimated/
ignored while positive benefits from the ocean arehighly evaluated.
This is a collaborative study with Professor Yoshiki
Yamagata, National Institute for Environmental Studies.
Daisuke Murakami

Figure 2: DSM and image of the 3D view evaluation.

Figure 3: Estimated non-linear influence ( x-axis: value of explanatory variables, y-axis: estimated influence).

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Research Center for Statistical Machine Learning

Time Series Data Mining Based on Event Analysis
N Statistical analysis of event cascades

Our project concerns modeling and inference of
event cascades, which ensue when events cause other events to occur, triggering further events. Example events in this context include chemical reactions,
posting and sharing content on social networking
services, etc. Because event cascades are universal
in a wide variety of systems, its comprehension is essential for understanding the emergence of complex
phenomena.
Mutually exciting point processes (i.e., Hawkes
processes) are widely used for modeling and analyzing event sequences. The rate of event occurrencesin these models is partitioned into two components:
a background rate, which describes exogenous effect (e.g., trends); and a mutually exciting component, in which events trigger an increase in the process rate. Owing to the latter component, Hawkes
processes exhibit rich dynamical behavior, in termsof event cascades. Much attention has recently been

Figure 1: Cumulative distribution function of cluster size ofchain-reaction bankruptcy.

paid to the field of social data mining. Modeling andinference of social networks, built upon Hawkes processes, form active research areas.
Whereas Hawkes processes describe series of
events in continuous time, events in real data are often aggregated within consecutive periods (e.g., dayor week), resulting in sequences of event count data
in discrete time units. Hawkes processes can be applied to analyze such data; however, it is more desirable to use a statistical model that directly accounts
for count data.
We proposed a statistical model for count sequence
networks that possesses a cascade structure. Basedon the proposed model, we develop an efficient statistical method to estimate the event cascades andthe model parameters. we applied our method tochain-reaction bankruptcy event data (Figure 1).
N Statistical analysis of neural data

Perception, memory, learning and decision making are processes carried out in the brain.
The performance of such intelligent tasks is madepossible by the communication of neurons (the cellsof which the brain consists) through sequences ofvoltage pulses called “spikes.”
I am interested in formulating methods of extracting information from the neuronal activity exhibited
while the brain carries out high-level processes (Figure 2). In particular, we formulated a power-law exhibited in the fluctuation of neuronal firing, based on
which we constructed a statistical model of spiketrains. We are developing a Bayesian decoding algorithm that extract information from neural data.
Shinsuke Koyama

Figure 2: Schematic diagram of neural coding.

8Research Centers

Efficient Iterative Method for Constrained Nonconvex Sparse Optimization
N Nonconvex optimization problem

A mathematical model for minimizing (or maximizing) a given objective function subject to constraints is called a mathematical optimization problem. A mathematical optimization problem in which
the objective function and the constraint set are convex is called a convex optimization problem. For any
convex optimization problem, a necessary condition
for optimality is also a sufficient condition. Thus, aglobal optimal solution is found by computing a solution that satisfies the necessary condition. On theother hand, a mathematical optimization problem
without the convexity above is called a nonconvex
optimization problem. For a nonconvex optimization
problem, a solution that satisfies the necessary condition may be a local optimal solution or a stationary
point. Although there are a number of studies on
global optimization, it is difficult to find a global optimal solution of a large-scale instance arising frommachine learning in general. As for machine learning, it is practically sufficient to obtain a solution

Figure 1: Our nonconvex sparse optimization problem.

that satisfies a necessary condition and has small
objective value.
N Nonconvex sparse optimization problem

A mathematical optimization problem with an additional term in the objective function or an additional constraint for finding a sparse solution with small
objective value is called a sparse optimization problem. The least squares problem with the l1 norm regularizer is well-known Lasso. Because the l1 norm
regularizer is convex, the resulting sparse optimization problem is convex if the original objective function and the constraint set are convex. Hence, we
can solve such a problem using techniques in convex
optimization. Recently, for finding more sparse solutions, nonconvex regularizers are often used. The resulting sparse optimization problem is nonconvex.
N Efficient iterative method for constrained

nonconvex sparse optimization
This year, we studied an efficient iterative method
for a convex constrained nonconvex sparse optimization problem (Figure 1). In this problem, we use acommonly used nonconvex sparse regularizer written as the difference of the l1 norm and a convex function. When we appropriately apply the DC algorithm,
which is an algorithm for solving a nonconvex optimization problem, we can find a stationary point bysolving a simple subproblem (Figure 2) repeatedly.
We found an efficient computation for solving thesubproblem for several constraint sets arising fromimportant applications (Figure 3). As a result, we
showed that our DC algorithm efficiently finds asparse stationary point with small objective value(Figure 4).
Mirai Tanaka

Figure 2: Subproblem arising from our DC algorithm.

Figure 3: Simple constraint sets.
Figure 4: Solution path corresponding tosparse solutions.

9Data Science Center for Creative Design and Manufacturing

Methodology Development for Data-Driven Materials Discovery
With state-of-the-art technologies in data science,
we aim to discover novel functional materials. Target
materials include drugs, dyes, solvents, polymers,
polymeric composites and nanostructured materials.
With the comprehensive technologies of machinelearning, such as Bayesian modeling, kernel methods, natural language processing, sparse learningand optimization theory, the mission of this researchis to create the fundamental methodology and research infrastructures of Materials Informatics.
N Role of data science in materials discovery

The design space of materials development is considerably high-dimensional. For instance, the chemical space of organic compounds consists of 1060
potential candidates. The challenge is to discovernovel materials from the huge landscape that exhibit
desirable material properties. In the traditional procedure, computational chemistry methods, such as
the first principle calculation, have been used as thecentral analytic tool. Scientists hypothesize material
structures based on experience and intuition, andproperties of the designed materials are assessedcomputationally and experimentally. With the great
success of machine learning in many other fields, scientists now realize that the data-driven approach isa promising alternative to achieve enormous savingson time and costs in the laborious, replacing the timeconsuming conventional trial-and-error procedure.
N Bayesian approach to data-driven materials

discovery
The aim of our study is to create a novel material
design method by the integration of machine learning and Bayesian inference. The method begins by
obtaining a set of machine learning models to predict properties of input material structures for multiple design objectives. These models are inverted tothe backward model through the Bayes’ theorem.
Then, we have a posterior probability distribution,
which is conditioned by the desired properties. Exploring high probability regions of the posterior, it isexpected to identify new materials possessing thedesired target properties.
Stephen Wu

Figure: Integration of Bayesian inference and machine for material design.

10Research Centers

Data Science Center for Creative Design and Manufacturing
N Mission statement
We aim to foster new scientific methodologies forinnovative design and manufacturing. Various fields inmanufacturing are now facing a revolutionary period.
Population reduction and globalization are bringing
dramatic changes in the industrial structure in Japan,
resulting in a rapid loss of global predominance in industry. Countries around the world has actively developed their growth strategies utilizing data science as adriving force, such as the Materials Genome Initiative in
the US and the Industry 4.0. Following the global trend
is no longer an effective way to survive in the intensive
power game around the world. In 2017, the Institute of
Statistical Mathematics has established a new research
center - Data Science Center for Creative Design and
Manufacturing. We have accumulated state-of-the-art
technologies in data science here, for instance, machine learning, Bayesian inference, materials informatics, and so on. We are devoted to foster and practice
innovative methods in data science for design and manufacturing through industry-academia collaboration.
N Smart manufacturing
The developments of new materials depend largely
on intuitions of highly experienced professionals, andtime-consuming trial-and-error processes for laboratory synthesis and testing of designed materials basedon computer simulation and experiments. On the other hand, recently, there are new attempts to substitutecomputational or real experiments in materials synthesis and testing by statistical models trained on given data. The enormous cost and time required in thecharacterization of material structures and physicalproperties has limited material studies within a small

set of candidates. For example, it has been proved thathigh throughput screening using techniques in data
science significantly increases the chance of discovering innovative functional materials. This is a basic concept of smart manufacturing in the perspective of data
science.
N Creative design and manufacturing
We recognize the importance of being at the absolute leading edge position in the manufacturing industry. This cannot be done by data science alone. Most of
the classical data science analysis tools are designed
for interpolating predictions. Data science used to be ascience of predictions based on pattern recognition
from existing data. For example, we often assume thatmaterials with similar chemical structure exhibit similar physical properties. However, by definition, a newmaterial is not likely to be similar to any of the existingmaterials. Combination of experiment, theory anddata science methods is an essential step to a new
breakthrough in the current state. In other words, we
adopt a stepwise approach to expand the region of accurate prediction of a statistical model. We achieve thegoal by careful design of an optimal experiment or simulation schedule for new data points that efficiently improve an existing predictive model. We have accomplished preliminary success in materials science using
an extrapolating prediction method based on a data
science approach, and fostered new collaboration opportunities between academia and industry for the
purpose of materials discovery. Our next step is to extend the application to various fields related to creative design and manufacturing.
Ryo Yoshida

Figure: Software developments for materials informatics.

11

Research Center for Medical and Health Data Science

Longitudinal Data Analysis in Medical and Health Data Science
N Longitudinal data analysis

N Comparison before and after intervention

In longitudinal data analysis, analytical methods
that take correlation or variance covariance into account have been developed. For example, a mixed
effects model with a random intercept is popular, but
this model is very simple. When the responses to anintervention vary across subjects, the model fit isbad, and the estimates tend to be biased dependingon the missing situation. The autoregressive linear
mixed effects model we propose provides a uniquevariance covariance structure considering the variation in responses to the intervention and the influence of the past treatment history. We are developing
methods for analyzing longitudinal data, aiming toelucidate dynamics such as the relationship between
the dose of drug and the efficacy and safety measurements over time.

in randomized controlled trials
It is said that the contribution of statisticians inmedicine is in research designs rather than statisticalmethods. Randomized controlled trials are conducted to ascertain whether a new treatment is effective.
This research design guarantees comparability between groups. Now, what analytical method should
be used in randomized controlled trials? The distributions of baseline values a​ re expected to be equal
between groups, but after intervention, not only theaverages but also the shapes of the distributions maychange. Some analytical methods are not robust to
the difference in shapes, and others are robust when
the numbers of subjects are equal between groups.
In this way, we are studying analytical methods related to research designs.
N Long term trends in health

Figure 1: Longitudinal data with dropouts ( left ). Longitudinal data in a randomized
controlled trial (right ).

Figure 2: Variance covariance matrices in autoregressive linear mixed effects
model ( left ) and mixed effects model with a random intercept ( right ).

12indicators
In preventive medicine, it is considered that the early life experience
may affect health at older age. In thiscase, a long-term perspective is important. Although repeated crosssectional surveys by random sampling have not been introduced muchin medical research designs, there
are surveys that last over 70 years,
and this research design can reveal
long-term trends in health indicators.
We are studying how to evaluate thelong-term impacts of changes in demographics and long-term lifestylefactors such as diet, exercise, smoking and drinking alcohol, and how to
use the findings to elucidate causes of
diseases and death to prevent them.
We aim to leave a healthier society to
the next generation.
Ikuko Funatogawa

Research Centers

Publication Bias in Meta-Analysis
N Meta-analysis in medical research

Meta-analysis is statistical analysis in which statistical results from some studies with a common purpose are combined to obtain an overall result withhigher evidence. Typical examples of meta-analysis
in medical research are found in clinical trials to verify effects of new drugs and epidemiological studies
to explore causes of some diseases. For example, in
randomized clinical trials where a new drug and an
existing drug are compared, it is possible to obtainan overall result to support the effect of the new drug,
even if each trial does not necessarily support it.
N Publication bias

In meta-analysis, the data from each study tends
to be biased since it can be obtained only from published sources. For example, statistically significant
results tend to be published in clinical trials and epidemiological studies with two-group comparison,and therefore the result of meta-analysis should bealso statistically significant if such published results
are combined. This sort of bias is called publication
bias. Figure 1 shows results of clinical trials to verify
that some drug prevents premature birth. The horizontal and vertical axes show estimates of log-odds

Figure 1: Funnel plot.

ratios and reciprocals of their standard errors fromthe trials. Since the standard errors are larger andthe results do not tend to be statistically significant
in the bottom, the skewed plot implies that there exists publication bias. This type of plot is called a funnel plot, which is often used to check existence of
publication bias.
N Worst-case evaluation

A strong assumption on the publication process isnecessary to adjust for publication bias. Since it cannot be verified from observed data, it is recommended to conduct a sensitivity analysis, where we check
how the overall results are changed according topossible change of the assumptions. However, this isa difficult problem and we made worst-case evaluation under the weaker assumption that statistical results with larger standard errors are more unlikely tobe published. Figure 2 shows the possible ranges ofoverall confidence intervals (real curves) and the upper bounds of overall P-values (dotted curves) by unpublished studies for meta-analysis of the data
shown in Figure 1. This figure implies that the significance of the overall result may change if the numberof unpublished studies is over 13.
Masayuki Henmi

Figure 2: Worst-case confidence intervals and P-values.

13

NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research
N Research and Educational Activities asa Biaxial Structureand Health Data Science (established on April 1, 2018).
These centers conduct research activities that interface
statistical mathematics with individual scientific disciplines in order to find solutions to urgent social problems.
The School of Statistical Thinking is devoted to professional development, and also provides multiple programs aimed at fostering and promoting statistical thinking. In the School of Statistical Thinking, researchers,
students, and contract researchers from private companies who seek to create a new statistical research field,
as well as various other people who recognize the necessity of statistics in their particular research field, train
together to foster statistical thinking. Of special importance, young project researchers receive on-the-job
training from their senior mentors and thereby obtainassorted skills related to statistical thinking. In 2017, we
initiated several programs for professional development
at various levels; these include the Big Data Analysis Research lecture and training program for engineers in private companies, and the Leading DAT (Leading Data Analytics Talents) lecture and training program, which was
designed specifically to train data scientists who haveknowledge and skills related to data-centric science inmodern society. Please refer to the page “Project of Fostering and Promoting Statistical Thinking” for more details.

School of Statistical Thinking

14

Research Center for Medicaland Health Data Science

Figure 1: Biaxial structure for research and education.

Data Science Center for Creative
Design and Manufacturing

Research Center for Statistical
Machine Learning

Research and Development
Center for Data Assimilation

Risk Analysis Research Center

The Institute of Statistical Mathematics (ISM) pursues
research and education along two lines of basic research,
as well as NOE (Network of Excellence)-type researchand professional development. Research and education
efforts are conducted by basic research departments
along a horizontal axis, and the NOE-type research centers and the school for professional development are organized along a vertical axis (Figure 1). By its nature, thebasic research departments (along the horizontal axis)
cuts across and links various disciplines, with the goal ofdeveloping tools for interdisciplinary research. The fieldof statistical mathematics must itself evolve to meet thechanging needs of society and the data environment,
and is therefore constantly evolving as a field of study. At
the same time, there are approaches and directions thathave remained unchanged as the field evolves. For thatreason, we have chosen not to refer to these efforts as“fundamental research” or “foundational research,” butinstead as “basic research”, in order to reflect both thefixed and evolving qualities of statistical mathematics.
There are three basic research departments: Statistical
Modeling, Statistical Data Science, and Statistical Inference and Mathematics. These departments engage incutting-edge research aimed at developing methodoloN NOE ( Network Of Excellence) Projectgies for rational prediction and decision making, basedon data and existing knowledge. We regularly assess
In accordance with the second medium-term plan forwhether our research system is functioning effectively
Research Organization of Information and Systems
from the viewpoints of research trends and prospects in(ROIS), ISM’s parent organization, ISM had set as a goal
statistical mathematics. Pursuant to these assessments,
the establishment of NOEs (Networks Of Excellence) inon April 1, 2018, we reorganized basic research departstatistical mathematics. This Japanese academic year
ments. All tenured research staff in ISM are assigned in(2018 –2019) is the ninth year since the beginning of theprinciple to one of these basic research departments.
project. We have reorganized to promote the project moreOn the other hand, the NOE-type research centerseffectively, considering the needs of each community as
and the school for professional development (along thewell as modern society as a whole.
vertical axis) are staffed by permanent researchers within ISM, project
professors/researchers (post-doctorNOE-type Research Centers
Professional
Research Support
Top-down
Development
Center for Eng. and Tec. Support
al staff), and visiting professors andresearchers. In addition, we reorgaBasic Research
nized the NOE-type research centers
Bottom-up
in Japanese academic year 2017–2018.
Dept.
ofStatistical
Five NOE-type research centers have
Modeling
been established: Risk Analysis ReDept. of Statisticalsearch Center, Research and DevelData Science
opment Center for Data Assimilation,
Research Center for Statistical MaDept. of Statistical
Inference and Mathematics
chine Learning, Data Science Centerfor Creative Design and Manufacturing, and Research Center for Medical

ISM Projects

We set up the new NOE-typeresearch center “Research Center for Medical and Health Data
Science” on April 1, 2018. Thiscenter aims to promote cuttingedge research and education in
the field of medical and healthscience, based on the research
network that ISM has establish­
ed in this field to date. As ofthis Academic year (2018 –2019),
the research activities of the
NOE Project have been expanded to six fields: Risk Research,
Next-generation Simulation, Survey Science, Statistical Machine
Learning, Data Science for Creative Design and Manufacturing,and Medical and Health Data
Science (Figure 2).

Risk Research NOE
Medical Health
Data Science NOE

Related Organizations

Related Organizations

Risk Analysis
Research Center

Research Center
for Medical and
Health Data Science

Next-Generation
Simulation NOE
Related Organizations

Research andDevelopment Center
for Data Assimilation

Advisory
Board

Managing
Committee
NOE Promotion Unit

Data Science Centerfor Creative Designand Manufacturing
Related Organizations

Data Science forCreative Design and
Manufacturing NOE

ISM
Research Center
for Statistical
Machine Learning

Survey Science Group,
Dept. of Statistical
Data Science
Related Organizations

Survey Science NOE
Related Organizations

Statistical Machine
Learning NOE

Advice from Advisory Board
Networks by concluded MoUs
Cooperative relations in ISM

Figure 2: Relationship diagram of the NOE Project.

N Future Conception of NOE Activities
To fulfill the goal of establishing new scientific methodologies (“Fourth Paradigm”) in a knowledge-based society, in which the importance of knowledge goes beyond merely solving individual problems, NOE activities
are being systematically pursued under the unified project guidelines formulated by the Managing Committee of
NOE Project. At the same time, advice is also taken fromthe members of the Advisory Board of NOE Project (Table 1), which is made up of experts from the industrial,academic, and government sectors.
The NOE Project, which is made possible by ISM’s
special focus in the cross-disciplinary field of “statisticalmathematics,” is attracting strong support from each ofthese communities. On the basis of this project, ISM, asan Inter-University Research Institute, will be providing
the industrial, academic, and government communities
Director of the Center for Materials
Research by Information Integration, MDIS,
National Institute for Materials Science

Dr. Satoshi Itoh

Professor Emeritus,
Tokyo Institute of Technology

Dr. Takatoshi Imai

Professor Emeritus,
The University of Tokyo

Dr. Yoshio Oyanagi

President, The Japan Pharmaceutical
Manufacturers Association

Mr. Yoshihiko Hatanaka

President, Toyota Technological
Institute at Chicago

Dr. Sadaoki Furui

Director-General, Institute for Monetaryand Economic Studies, Bank of JapanMr. Shigenori Hiratsuka

Table 1: Advisory Board of NOE Project. ( As of April 1, 2018 )with further opportunities for joint usage (of facilities)
and joint research. ISM continues to promote this NOE
Project.
As described above, the NOE-type research centers
and the group serve as core hubs in their respective
fields. ISM is promoting the signing of MOUs (Memorandum of Understanding) with research organizations
within Japan and overseas, and the number of MOUs isincreasing each year, including MOUs that span multiple
NOE research fields. The goal of ISM is general researchin statistical mathematics, which is in demand by various research fields in both the humanities and science.
ISM must respond flexibly to requests from each community and contribute to them. Reorganization over recent
years, based on the needs of each community, has aimed
at much deeper promotion of the NOE Project.
With its focus on methodology, ISM continues to plan
research in each of the five NOE research fields. Furthermore, with all research activities concentrated under theumbrella of NOE Research Promotion, the general bodythat oversees the five NOEs, ISM is expanding and developing its NOE projects with the aim of establishing newscientific methodologies (“Fourth Paradigm”), creating
new research disciplines, and developing new styles ofjoint research. For up-to-date information on the research structures of the NOE Project, please visit thewebsite. We very much appreciate your continued support for this Project.

http://www.ism.ac.jp/noe/project/en/

15

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data. Now
surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is a need fordata scientists who can handle such big data and are able to extract useful knowledge from it. Meanwhile,
Japanese higher education is exhibiting a deplorable lack of production capacity in terms of data scientists. This can be accounted for by the fact that no academic institution other than ISM has a Ph. D. course
in statistics and the small number of statisticians in academia are isolated from each other, being scatteredover various disciplines. Hoping to gain a little traction on this problem, ISM established the School of Statistical Thinking, into which we integrated all of our educational resources. In FY 2016, ISM established theManaging Committee of School of Statistical Thinking, inviting contributions from outside experts, and in
FY 2017 we launched the Leading DAT program by adopting the suggestions by the committee. The following are the principal projects.

Research Collaboration Startup
The Institute had already been providing a consultation service for statistical science, but alongwith the launch of the School of Statistical Thinking in November 2011, this service was reorganized
as a research collaboration startup. This program,being one of the projects to foster and promotestatistical thinking, is mainly aimed at supporting
applied scientists and other non-experts. Expert

statisticians affiliated with the Institute give them
advice on statistical modeling, data analysis, andresearch. Some cases have developed into official
research collaborations, which are our primary
duty as an inter-university research institute. The
Institute accepts more than 50 cases annually, someof which benefit society in diverse ways.

Open-type Professional Development ProgramThis is a spin-out program from ISM cooperative
research projects. Establishing a goal is an indispensable element of the proposal of a cooperative
research project. On the other hand, such goal
setting is irrelevant for a summer school program,study session, or retreat. Since the launch of the
School of Statistical Thinking, organizers of such

Open Lecture
We hold an open lecture during Education andCulture Week every year (November 1 through7), to introduce the Institute’s activities and topromote statistical science. We invite lecturers
to speak on a timely topic relating to statisticalscience. The lecture is open to the general public.
For further information, please visit the website of
the Institute of Statistical Mathematics.
http://www.ism.ac.jp/kouenkai/
16group-oriented study programs can apply to theOpen-type Professional Development Program.
There are two categories under this program: oneis ‘workshop’ and the other is ‘intensive training foryoung researchers’. For FY 2017, five workshops
have been accepted after review.

ISM Projects

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on
statistical mathematics every Wednesday. The
seminars are led by in-house and external lecturers to showcase their latest studies. These lectures

are free to attend. To view the seminar scheduleand learn more about the program, please visit
the Institute of Statistical Mathematics website.
http://www.ism.ac.jp/index_e.html

Data Science Research Plaza
Researchers funded by private-sector firms canmaintain a desk and phone in the School of Statistical Thinking. This program is subject to fees,
and the contract can be renewed annually. A faculty mentor gives advice to the accepted funded
researcher so that he or she can freely attend var-ious events, such as seminars, workshops, conferences, and extension courses. After learning theexpertise of the ISM research staff, participants inthis program are invited to take advantage of paid
consultations and funded research collaboration.

Leading DATIn FY 2017, the School of Statistical Thinking
launched a program called “Leading DAT” aimed
at training data scientists with the knowledge andskills in statistical mathematics required by modern society. As the program’s first projects, we organized two Leading DAT lectures entitled “L-B1
Bayesian Modeling in Practice” and “L-B2 Machine
Learning and Modern Methodologies in Data Science.” At the same time, we established the Leading DAT Training Course, in which we grant certificates to participants who have fulfilled the course
requirements, including attendance in all lecturesand submission of reports. A total of 25 people
have been granted the certificate of completion.

Certificate ceremony

Tutorial Courses
The education program at ISM dates back to
1944, the year of founding. The Ministry of Education installed a training center within ISM to foster
technicians in numerical computation. After theWorld War II, this training center was relaunched
in 1947 to develop pollsters and census takers. It
helped to cultivate professionals in the field of statistical surveys, while a growing number of entries
from business and industry coming for various
types of training were also observed.
Now the tutorial courses are operated by the

School of Statistical Thinking, which was established in 2011.
In the 2017 academic year, 13 courses (including
Leading DAT Lectures) were held and the numberof participants was 968. The total number of courses held from 1969 to March, 2018 was 362, with a
total of 25,991 participants. These courses covereda wide range of fields from basic to applied statistics. The schedule of tutorial courses can be foundon the website of the Institute of Statistical Mathe­
matics.
http://www.ism.ac.jp/lectures/kouza.html
17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census

USA ( Washington )July 27, 1988

Stichting Mathematisch Centrum

The Kingdom of the Netherlands( Amsterdam )May 10, 1989

Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany ( Berlin )December 8, 2004

The Steklov Mathematical InstituteRussia ( Moscow )August 9, 2005

Central South University

China ( Changsha )November 18, 2005

Soongsil University

The Republic of Korea ( Seoul )April 27, 2006

University of Warwick

The United Kingdom ( Coventry )January 16, 2007

Indian Statistical Institute

India ( Kolkata )October 11, 2007

Institute of Statistical Science, Academia Sinica

Taiwan ( Taipei )June 19, 2008

Department of Empirical Inference,
Max Planck Institute for Biological Cybernetics

Germany ( Tubingen )August 11, 2010

Department of Communication Systems,
SINTEF Information and Communication Technology

Norway ( Trondheim )January 30, 2012

University College London

The United Kingdom ( London )February 16, 2012

Department of Electronics and Telecommunications,
Norwegian University of Science and Technology

Norway ( Trondheim )May 22, 2012

Department of Probability and Mathematical Statistics,
Charles University in Prague

Czech Republic (Prague )October 10, 2012

Department of Ecoinformatics,
Biometrics and Forest Growth of the Georg-August University of Goettingen

Germany (Goettingen )October 18, 2012

Korean Statistical Society (KSS )The Republic of Korea ( Seoul )July 9, 2013

Toyota Technological Institute at Chicago

USA ( Chicago )February 10, 2014

Australian National University

Australia ( Canberra )May 15, 2014

RiskLab ETH Zurich

Switzerland ( Zurich )February 7, 2015

Institut de Recherche en Composants logiciel et materiel pour l’Information
et la Communication Avancee

France ( Paris )February 9, 2015

Le laboratoire de mathematiques de I’Universite Blaise Pascal

France (Clermont-Ferrand )February 11, 2015

Centre de Rechereche en Informatique,
Signal et Automatique de Lille

France ( Paris )February 12, 2015

University College London Big Data Institute

The United Kingdom ( London )February 26, 2015

The Institute of Forestry, Pokhara of Tribhuvan University

Nepal ( Pokhara )March 6, 2015

The Institute of Forest and Wildlife Research and Developmentof the Forestry Administration of Cambodia

Cambodia ( Phnom Penh )March 6, 2015

The Chancellor masters and Scholars of the University of Oxford

The United Kingdom ( Oxford )March 10, 2015

Forest Inventory and Planning Institute of Vietnam

Vietnam ( Hanoi )June 2, 2015

Zuse Institute Berlin

Germany (Berlin )June 20, 2015

The University of Porto

Portugal ( Porto )June 22, 2015

Natinonal University of Laos

Laos ( Vientiane )March 15, 2017

Institute of Geophysics China Earthquake Administration

China ( Beijing )April 28, 2017

Hong Kong Baptist University

Hong Kong ( Kowloon Tong )August 28, 2017

University of Malaya

Malaysia ( Kuala Lumpur)

September 18, 2017

Unversidade de Evola

Portugal ( Evola )November 30, 207

Universität Ulm

Germany ( Ulm )December 8, 2017

The Korean Association for Survey Research

The Republic of Korea ( Seoul )February 14, 2018

* There are two more agreements concluded.

18

Research Collaboration
ISM performs many activities for collaborating with researchers in the various fields of statistical science,from the individual level to the national level. The ISM cooperative research program regularly performs research activities to provide the research resources of ISM to researchers at universities or research institutes inorder to advance their academic research. Available research resources include books, journals, supercomputers, some commercial statistical software packages, as well as statistical packages developed by ISM, and also
the researchers in ISM themselves, who have abundant professional knowledge and experience in statisticalscience and data analysis. The ISM cooperative research program provides not only research support funds
but also opportunities for the various researchers in many fields who require statistical knowledge to make useof the resources available at ISM. ISM’s aim is to be a place for interaction and fusion among researchers insideand outside of ISM, and to contribute to multidisciplinary development of both the theory and the applicationof statistical science.

■ Number of Activities
Year

2012

2013

2014

2015

2016

2017

Number of Activities

182

181

177

183

187

161

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to findthe most appropriate type of project.
ISM Fields
NumberFieldsNumberFieldsa

Spatial and Time Series Modeling Groupf

Structure Exploration Groupb

Complex System Modeling Groupg

Mathematical Statistics Groupc

Latent Structure Modeling Grouph

Learning and Inference Groupd

Survey Science Groupi

Computational Inference Groupe

Metric Science Groupj

OthersMajor Research Fields
NumberFieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, law, politics, society, manage­ment, official statistics, population, etc.

8

Environmental science

Environmental Statistics, Environmentrics, Agricultural Statistics, Statistical Meteorology,
Land Economics, Landscape Management, Forest Management, etc.

9

OthersOther research fields

19Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (The Graduate University for
Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its opening, the Institute has included the Department of Statistical Science and, sinceApril 1989, has accepted students for education and research in doctoral programs. In 2006, the Instituteadopted a five-year system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

SOKENDAI

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The Department of Statistical Science, which is based on the Institute of Statistical Mathematics (ISM) serving
as its underlying platform, aims to cultivate individuals who possess creative research skills to contribute tosolving various important intricately-intertwined problems. To this end, the Department conducts education
and research related to the basis, mathematics and applications of data collection designs, modeling, inference
and prediction, and equip students with the ability to extract information and knowledge from the real world
based on the effective use of data.
Field of Education and Research

20

Contents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical Analysis
and Statistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science by professors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well
as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of thebest in the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible forstudents to develop their own projects by participating in research projects with other institutions.

Course Requirements and Type of Degree Granted● Requirements to complete the doctoral course are as follows:

Completion of at least 40 credits, including the required ones, by a student in the five-year program, or completion of at least 10 credits by a three-year doctorate student who previously completed a Master’s course;
meeting all the criteria set by the thesis committee of the Institute; and successfully completing the examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with aninter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research results.

Number of Students ( As of April 1, 2018)■ Doctor’s course

five years:Quota,2■ Doctor’s course

three years:Quota,3Year of enrollment

2010

2013

2014

2016

2017

2018

Number of students

1

1

1

1

2

1

Year of enrollment

2013

2014

2015

2016

2017

2018

Number of students

1

3

1

5

4

4

* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University ( 4 ) • Tohoku University ( 3 ) • Fukushima University (1) • University of Tsukuba (6 ) • Saitama University (1)• Chiba University (1) • Ochanomizu University (1) • National Graduate Institute for Policy Studies (1) • Tokyo Medical and Dental
University (1) • Tokyo University of Marine Science and Technology (1) • Tokyo Gakugei University ( 2 ) • Tokyo Institute ofTechnology ( 5 ) • The University of Tokyo (14 ) • Tokyo Metropolitan University (1) • Tokyo University of Agriculture and Technology (1)• Hitotsubashi University (6 ) • Shizuoka University (1) • Japan Advanced Institute of Science and Technology (1) • Nagoya University (3 )• Toyohashi University of Technology ( 2 ) • Kyoto University ( 7 ) • Osaka City University (1) • Osaka University ( 3 ) • Nara Institute of
Science and Technology (1) • Okayama University ( 2 ) • Shimane University (3 ) • Kyushu University ( 2 ) • Oita University (1)21Graduate School Program

University Background of Students
Private universities• Kitasato University (1) • Keio University (8 ) • Shibaura Institute of Technology (1) • Chuo University (8 ) • Tokyo University of
Science (7 ) • Toyo University (1) • Japan Women’s University (1) • Nihon University (2 ) • Hosei University (7 ) • Waseda University (9 )• Nanzan University (1) • Kansai University (1) • Kyoto Sangyo University (1) • Ritsumeikan University (1) • Okayama University of
Science (1) • Kurume University (1)Foreign universities• Aston University (1) • University of California, Irvine (1) • California State University, Long Beach (1) • University of Campinas (1)• University of Colorado Boulder (2 ) • University of Dhaka (2) • University of Hawaii (1) • Jahangirnagar University (2)• University of Malaya (1) • Northeast Normal University (1) • Ohio University (2) • University of Rajshahi (2) • Stanford University (1)• The University of Nottingham (1) • Institute of Applied Mathematics, AMSS, CAS (1) • University of Science and Technology ofChina (1) • Center for Analysis and Prediction, China Seismological Bureau (1) • Northeastern University (1) • The Hong Kong
University of Science and Technology (1)Degrees Awarded
Year

2012

2013

2014

Doctor of Philosophy

6 [1]

6

5

2015

2016

2017

5

7

5

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute ofTechnology • University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University• Hokkaido University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences• JAXA’s Engineering Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan• Japan Broadcasting Corporation • Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon
University • Komazawa University • Aichi University of Technology • Tokyo University of Information Sciences • Shibaura Instituteof Technology • Rikkyo University • Waseda University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor
Corporation, Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating
Organization of Japan • Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs
Asset Management L.P. • CLC bio Japan, Inc. • Bank of Tokyo-Mitsubishi UFJ • Pfizer Japan Inc. • Doctoral Institute for Evidence
Based Policy • Sony Corporation • NTTIT Corporation • Sompo Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone
Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd. • PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma
Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center • CPC Clinical Trial Hospital, Medipolis Medical Research Institute• CRD Association • Japan Society for the Promotion of Science • Tokyo Electric Power Company Holdings, Inc. • Asahi Kasei
Corporation • Honda R&D Co.,Ltd. • Yokogawa Electric Corporation • Kao Corporation

22

Facilities and Equipment

Computational Resources ( As of April 1, 2018 )ISM is now maintaining two different supercomputer
systems. One is the Supercomputer System for Statistical
Science (called System “I”) which is going to be replaced
by a new one during FY2018. The other system is Communal Cloud Computing System (called System “C”) introduced at the end of FY2013.
System “ I ” is a large distributed-memory supercomputer that consists of 400 nodes of SGI ICE-X with Ivy
Bridge and 136 Haswell nodes, having 12,864 cores intotal. The system also includes a large-scale sharedstorage system (2.5 PB disk storage) physical random
number generator boards, and a visualization systemthat supports the maximum resolution of 4,096 * 2,160
and has a 200-inch rear projection screen for 3D visualization.
ISM is going to replace the present System “ I ” by a
new one, HPE SGI 8600 Supercomputer System during
FY2018. The new system will have nearly four times
computing power, and the total main memory will expand to 144TB. It will also have several accelerator
nodes each of which has four NVIDIA Tesla P100 boards.
System “C” consists of 64 Dell PowerEdge R620 (twoThe Supercomputer System for
Statistical Science

10-core Xeon E5-2680v2, 256GB memory). This systemprovides easy-to-use computing environments such as
distributed-memory statistical computing environments
and Web servers, running on Apache CloudStack software. This private cloud system is also used in hostingdata analysis competition events.
In the main office, the primary local area network(LAN) consists of an Ethernet network using 10GBase-SR
for the main trunk and 1000Base-T for branches. The
personal computers in researchers’ offices, and the systems I and C are all connected to this network. A wireless
LAN system is also available in the immediate area ofthe building occupied by the institute. These LAN systems enable distributed processing and allow computing resources and statistical data to be used effectively.
Comprehensive network security methods have been
implemented, such as a firewall system, anti-virus software, and an intrusion prevention system. To encourage joint research with researchers both in Japan andabroad, as well as the exchange of e-mails, the network
is connected to the Internet through SINET5 (40 Gbps).

The Communal Cloud Computing
SystemSupercomputer System
HPE SGI 8600

Library and Materials ( As of April 1, 2018 )We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.
Japanesebooks
19,59428%

Books
70,645

Japanese
periodicals
1,181
53%

Foreignperiodicals

Periodicals
2,224

1,043
47%

Foreign books
51,051
72%

23

Finance and Buildings

Administration Subsidy and Others ( 2017 )Type

Personnel expenses

Non-personnel expenses

Total

704,938

926,658

1,631,596

Expenditure

Unit: 1,000JPY

Accepted External Funds ( 2017 )
Type

Joint
research

Subcontracted
research,
Trustee businessContract
researchers

Academic
Consulting

Contribution for
scholarship

Total

Items

25

18

6

6

8

63

Income38,802

135,391

1,533

4,193

9,859

189,778
Unit: 1,000JPY

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2017 )Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Innovation Areas

1

10,790Grant-in-Aid for Scientific Research ( S )——Grant-in-Aid for Scientific Research ( A)

4

40,040Grant-in-Aid for Scientific Research ( B)931,070Grant-in-Aid for Scientific Research ( C)23

29,783Grant-in-Aid for Challenging Exploratory Research

1

1,040

Grant-in-Aid for Young Scientists ( B )

11

8,745

Grant-in-Aid for Research Activity Start-up

2

2,730

Grant-in-Aid for JSPS Fellows

3

4,550

54

128,748

Total

Unit: 1,000JPY

Site and Buildings ( As of April 1, 2018 )24

Site Area

62,450m 2

Area for Buildings (total)

16,209m 2

Organization

Organization Diagram ( As of April 1, 2018)■ Prediction and Control Group

Cooperative
Research
Committee

Department of Statistical Modeling■ Data Assimilation Group

Department of Statistical
Data Science
Council
Department of Statistical
Inference and Mathematics
Advisory
Board■ Complex System Modeling Group■ Survey Science Group■ Metric Science Group■ Structure Exploration Group■ Mathematical Statistics Group■ Learning and Inference Group■ Mathematical Optimization Group

Risk Analysis Research Center
Research and Development
Center for Data Assimilation

DirectorGeneral

Vice DirectorGeneral

Research Center for
Statistical Machine Learning
Data Science Center for Creative
Design and Manufacturing
Research Center for Medical and
Health Data Science
School of Statistical Thinking■ Computing Facilities Unit

Center for Engineering andTechnical Support■ Computer Networking Unit
■ Information Resources Unit
■ Media Development Unit

Library

Planning Unit

■ URA Station

Evaluation Unit
Information and Public Relations Unit
Administration Planningand Coordination Section

Industry-Academia Collaboration and Intellectual Property Unit
NOE Promotion Unit
Gender Equality Unit
International Affairs Unit

25

Number of Staff ( As of April 1, 2018 )DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Staff
Professor Professor rative Staff

Total

1

1

Department of Statistical Modeling

6

7

2

15

Department of Statistical Data Science

5

6

4

15

Department of Statistical Inference and Mathematics

7

5

3

15

1

1

School of Statistical Thinking
Center for Engineering and Technical Support

11

11

Administration Planning and Coordination Section

1

1

Tachikawa Administration Department

( 35)

( 35)

Total

1

18

18
(

10

1 ( 35)1159 ( 35)) Total number of staff of Tachikawa Administration Department.

The number under Technical Staff at the Center for Engineering and Technical Support and Administrative Staff at the Tachikawa Administration Department
include two each staff member who retired because of age but was reemployed in a different position.

Staff ( As of August 1, 2018 )Director-General

Tomoyuki HIGUCHI

Vice Director-General

Satoshi ITO

Vice Director-General

Satoshi YAMASHITA

Vice Director-General

Koji KANEFUJI

Department of Statistical Modeling
Director Junji NAKANO• Prediction and Control Group ————————————————————————————————————————————————————————————
Prof.

Yoshinori KAWASAKIProf.

Yoshihiko MIYASATO

Prof.

Atsushi YOSHIMOTO

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI• Complex System Modeling Group ——————————————————————————————————————————————————————————Prof.

Yukito IBA

Assoc. Prof.

Kazuhiro MINAMI

Assoc. Prof.

Hideitsu HINO

Assist. Prof.

Momoko HAYAMIZU

Assoc. Prof.

Shinsuke KOYAMA• Data Assimilation Group ———————————————————————————————————————————————————————————————Prof.

Tomoyuki HIGUCHIProf.

Junji NAKANOProf.

Genta UENO

Assoc. Prof.

Shinya NAKANOAssist. Prof.

Shunichi NOMURA

Visiting Assoc. Prof. Yuji MIZUKAMI

Department of Statistical Data Science
Director Tomoko MATSUI• Survey Science Group —————————————————————————————————————————————————————————————————————

26

Prof.

Ryozo YOSHINO

Assoc. Prof.

Assist. Prof.

Masayo HIROSE

Project Assist. Prof. Yusuke INAGAKITadahiko MAEDA

Project Researcher

Naoko KATO

Visiting Prof.

Takatoshi IMADA

Assoc. Prof.

Yoo Sung PARK

Project Assist. Prof. Kiyohisa SHIBAIVisiting Prof.

Toru KIKKAWA

Organization

Staff
Department of Statistical Data Science
Visiting Prof.
Visiting Prof.

Yoshimichi SATO

Visiting Prof.

Saeko KIKUZAWA

Visiting Assoc. Prof. Koken OZAKI

Wataru MATSUMOTO

Visiting Prof.

Masahiro MIZUTA

Visiting Assoc. Prof. Tadayoshi FUSHIKI

Visiting Assoc. Prof. Taisuke FUJITA• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Prof.

Koji KANEFUJI

Prof.

Yoichi ITO

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Hisashi NOMA

Assist. Prof.

Nobuo SHIMIZU

Project Researcher

Hiroka HAMADA

Visiting Prof.

Shizue IZUMI• Structure Exploration Group —————————————————————————————————————————————————————————————Prof.

Tomoko MATSUIProf.

Ryo YOSHIDA

Assoc. Prof.

Jun ADACHI

Assoc. Prof.

Kenichiro SHIMATANI

Assist. Prof.

Stephen WU

Assist. Prof.

Daisuke MURAKAMI

Department of Statistical Inference and Mathematics
Director Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Prof.

Yoshiyuki NINOMIYAAssoc. Prof.

Shuhei MANO

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Takaaki SHIMURA

Assist. Prof.

Teppei OGIHARA

Visiting Prof.

Akimichi TAKEMURA• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHIProf.

Kenji FUKUMIZU

Prof.

Hironori FUJISAWA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Masayuki HENMI

Assist. Prof.

Ayaka SAKATA• Mathematical Optimization Group —————————————————————————————————————————————————————————
Prof.

Satoshi ITO

Visiting Prof.

Eitarou AIYOSHIProf.

Shiro IKEDA

Assist. Prof.

Mirai TANAKA

Risk Analysis Research Center
Director Satoshi YAMASHITA

Vice Director Shogo KATO

Prof.

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Shinto EGUCHIProf.

Koji KANEFUJI

Prof.

Atsushi YOSHIMOTO

Prof.

Yoshinori KAWASAKI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Hisashi NOMA

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Kazuhiro MINAMI

Assoc. Prof.

Takaaki SHIMURA

Assist. Prof.

Teppei OGIHARA

Assist. Prof.

Shunichi NOMURA

Project Assist. Prof. Shizu ITAKA

Project Assist. Prof. Hayafumi WATANABE

Project Assist. Prof. Yuta TANOUE

Project Assist. Prof. Takao KUMAZAWA

Project Assist. Prof. Junchao ZHANG

Project Assist. Prof. Yicun GUO

Project Researcher

Yuma UEHARA

Project Researcher

Hideaki NAGAHATA

Visiting Prof.

Rinya TAKAHASHI

Visiting Prof.

Toshikazu KITANO

Visiting Prof.

Yo SHIINA

Visiting Prof.

Naoki SAKAI

Visiting Prof.

Mihoko MINAMI

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Toshihiro HORIGUCHI

Visiting Prof.

Shunji HASHIMOTO

Visiting Prof.

Yasuhiro KUBOTA

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Toshio HONDA

Visiting Prof.

Hideatsu TSUKAHARA

27

Risk Analysis Research Center
Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Michiko MIYAMOTO

Visiting Prof.

Tadashi ONO

Visiting Prof.

Satoshi FUJII

Visiting Prof.

Takaaki YOSHINO

Visiting Prof.

Masakazu ANDO

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Shinsuke ITO

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Masaaki FUKASAWA

Visiting Prof.

Yasutaka SHIMIZU

Visiting Assoc. Prof. Hisayuki HARA

Visiting Assoc. Prof. Takashi KAMEYA

Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Tetsuji TONDA

Visiting Assoc. Prof. Takaki IWATA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Kazuyoshi NANJO

Visiting Assoc. Prof. Masao UEKI

Visiting Assoc. Prof. Yukihiko OKADA

Visiting Assoc. Prof. Seisho SATO

Visiting Assoc. Prof. Junichi TAKAHASHI

Visiting Assoc. Prof. Yuta KOIKE

Visiting Assoc. Prof. Takafumi KUBOTA

Research and Development Center for Data Assimilation
Director Genta UENO

Vice Director Shinya NAKANOProf.

Genta UENOProf.

Tomoyuki HIGUCHIProf.

Junji NAKANOProf.

Yukito IBAProf.

Ryo YOSHIDA

Assoc. Prof.

Shinya NAKANO

Project Assoc. Prof. Masaya SAITOAssist. Prof.

Stephen WU

Assist. Prof.

Shunichi NOMURA

Assist. Prof.

Daisuke MURAKAMI

Project Researcher

Takashi YAMAMOTO

Visiting Prof.

Kazuyuki NAKAMURA

Visiting Prof.

Shinichi OTANIVisiting Prof.

Yoichi MOTOMURA

Visiting Prof.

Nobuhiko TERUIVisiting Prof.

Tadahiko SATO

Visiting Prof.

Masako KAMIYAMA

Visiting Assoc. Prof. Hiromichi NAGAO

Visiting Assoc. Prof. Hiroshi KATO

Visiting Assoc. Prof. Eiji MOTOHASHI

Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Yosuke FUJII

Research Center for Statistical Machine Learning
Director Kenji FUKUMIZU

Vice Director Tomoko MATSUI

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Shinto EGUCHIProf.

Yoshihiko MIYASATO

Prof.

Satoshi ITOProf.

Shiro IKEDA

Prof.

Satoshi KURIKI

Prof.

Hironori FUJISAWA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Shinsuke KOYAMA

Assoc. Prof.

Kazuhiro MINAMI

Assoc. Prof.

Mirai TANAKA

Assist. Prof.

Daisuke MURAKAMI

Project Assist. Prof. Mikio MORII

Project Researcher

Jin ZHOU

Visiting Prof.

Katsuki FUJISAWA

Visiting Prof.

Takashi TSUCHIYA

Visiting Prof.

Yuji SHINANO

Visiting Prof.

Akiko TAKEDA

Visiting Prof.

Masataka GOTO

Visiting Prof.

Yoshiki YAMAGATA

Visiting Prof.

Arthur GRETTON

Visiting Assoc. Prof. Makoto YAMADA

Prof.

Project Assist. Prof. Matthew Christopher AMES

Visiting Assoc. Prof. Tsutomu TAKEUCHI

Data Science Center for Creative Design and Manufacturing
Director Ryo YOSHIDA

28

Vice Director Hironori FUJISAWAProf.

Ryo YOSHIDA

Prof.

Hironori FUJISAWA

Prof.

Kenji FUKUMIZU

Assoc. Prof.

Shinya NAKANOAssoc. Prof.

Daichi MOCHIHASHI

Assist. Prof.

Stephen WU

Project Researcher

Hironao YAMADA

Visiting Assoc. Prof. Terumasa TOKUNAGA

Organization

Staff
Research Center for Medical and Health Data Science
Director Yoichi ITO

Vice Director Hisashi NOMAProf.

Yoichi ITOProf.

Satoshi YAMASHITA

Prof.

Shinto EGUCHI

Assoc. Prof.

Hisashi NOMA

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Fumikazu MIWAKEICHI

Project Assoc. Prof. Kengo NAGASHIMA

Visiting Prof.

Yasuo OHASHI

Visiting Prof.

Senichiro KIKUCHI

Visiting Prof.

Ken KIYONO

Visiting Prof.

Toshiya SATO

Visiting Prof.

Satoshi HATTORI

Visiting Prof.

Tatsuhiko TSUNODA

Visiting Prof.

Satoshi TERAMUKAI

Visiting Prof.

Hisateru TACHIMORI

Visiting Prof.

Manabu IWASAKI

Visiting Prof.

Shusaku TSUMOTO

Visiting Prof.

Michiko WATANABE

Visiting Assoc. Prof. Noriko TANAKA

Project Assist. Prof. Mayumi OKA

Visiting Assoc. Prof. Ryoichi KIMURA

Visiting Assoc. Prof. Kunihiko TAKAHASHI

Visiting Assoc. Prof. Kazushi MARUO

Visiting Assoc. Prof. Atsushi GOTO

Visiting Assoc. Prof. Masataka TAGURI

Visiting Assoc. Prof. Ryota NAKAMURA

School of Statistical Thinking
Director Yoshinori KAWASAKI

Vice Director Genta UENO
Vice Director Yukito IBA

Prof.

Satoshi ITO

Prof.

Yukito IBA

Prof.

Kenji FUKUMIZU

Project Prof.

Yoshiyasu TAMURA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Assist. Prof.

Masaaki IMAIZUMIProject Assist. Prof. Mitsuru TOYODA

Visiting Assoc. Prof. Naoki KAMIYA

Center for Engineering and Technical Support
Director
Deputy Manager

Yoshinori KAWASAKI

Vice Director

Jun ADACHI

Yuriko WATANABE

Unit Leader of Computing Facilities Unit

Mitsuru HAYASAKA

Unit Leader of Computer Networking Unit

Kazuhiro NAKAMURA

Unit Leader of Information Resources Unit

Yuriko WATANABE

Unit Leader of Media Development Unit

Akiko NAGASHIMA

Library
Head Yoshinori KAWASAKI

Administration Planning and Coordination Section
Chief Director Tomoyuki HIGUCHI
Director of Planning Unit

Satoshi ITO

Director of Information and Public Relations Unit Koji KANEFUJI
Director of NOE Promotion Unit

Satoshi ITO

Director of International Affairs Unit

Junji NAKANO

Director of Evaluation Unit

Koji KANEFUJI

Director of Industry-Academia Collaboration
and Intellectual Property Unit

Satoshi YAMASHITA

Director of Gender Equality Unit

Satoshi ITO• URA Station ——————————————————————————————————————————————————————————————————————
Leader

Kozo KITAMURA

Research Administrator

Keisuke HONDA

SubleaderMotoi OKAMOTO

29

Council of The Institute of Statistical Mathematics ( As of April 1, 2018 )Keiko TAKAHASHI

Director-General, Japan Agency for Marine-Earth Science and Technology ( JAMSTEC ) Center
for Earth Information Science and Technology ( CEIST )Masahiro MIZUTA

Professor, Information Initiative Center Hokkaido University Graduate School of Information Science and Technology,
Hokkaido University

Shigeru OBAYASHI

Director, Institute of Fluid Science, Tohoku University

Nakahiro YOSHIDA

Professor, Graduate School of Mathematical Sciences, University of Tokyo

Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Hiroshi SAIGO

Professor, Faculty of Political Science and Economics, Waseda University

Yasuhiro OMORI

Professor, Faculty of Economics, University of Tokyo

Kikuo MAEKAWA

Professor, Spoken Language Division, Director,
Center for Corpus Development National Institute for Japanese Language and Linguistics

Hideki ASOH

Director, National Institute of Advanced Industrial Science and Technology,
Department of Information Technology and Human Factors Artificial Intelligence Research Center Deputy

Mihoko MINAMI

Professor, Department of Mathematics, Faculty of Science and Technology, Keio University

Ryozo YOSHINO

Professor, Research Organization of Information and Systems

Satoshi ITOProfessor ( Vice Director-General, ISM )Satoshi YAMASHITA

Professor ( Vice Director-General, ISM )Koji KANEFUJI

Professor ( Vice Director-General, ISM )Junji NAKANOProfessor ( Director of Department of Statistical Modeling, ISM )Tomoko MATSUI

Professor ( Department of Statistical Modeling, ISM )Satoshi KURIKI

Professor ( Director of Department of Mathematical Analysis and Statistical Inference, ISM )Yoshinori KAWASAKI

Professor ( Director of Center for Engineering and Technical Support, ISM )Yoshihiko MIYASATO

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Yukito IBA

Professor ( Department of Statistical Modeling, ISM )Kenji FUKUMIZU

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Cooperative Research Committee ( As of April 1, 2018 )30

Tadahiko SATO

Professor, Faculty of Business Sciences, University of Tsukuba

Kunihiko TAKAHASHI

Associate Professor, Department of Biostatistics, Nagoya University Graduate School of Medicine

Akinobu TAKEUCHI

Professor, Faculty of Humanities and Social Sciences, Jissen Women's University

Takahiro TSUCHIYA

Professor, Center for Data Science, Yokohama City University

Hiroyuki MINAMI

Professor, Information Initiative Center, Hokkaido University

Junji NAKANOProfessor ( Director of Department of Statistical Modeling, ISM )Koji KANEFUJI

Professor ( Director of Department of Data Science, ISM )Shiro IKEDA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Shinto EGUCHI

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Organization

Managing Committee of School of Statistical Thinking ( As of April 1, 2018 )Satoshi IMURA

Professor, Bioscience Group, National Institute of Polar Research

Yoshimichi OCHI

Trustee, Vice-president, Oita University

Takafumi KUSANO

BrainPad Inc., Representative Director/Co-Founder

Wataru SAKAMOTO

Professor, Graduate School of Environmental and Life Science, Okayama University

Yo SHEENA

Professor, Academic Assembly School of Humanities and Social Sciences Institute of Social Sciences, Shinshu University

Yoshinori KAWASAKI

Director ( School of Statistical Thinking, ISM )Yukito IBA

Vice Director ( School of Statistical Thinking, ISM )Genta UENO

Vice Director ( School of Statistical Thinking, ISM )Satoshi ITOProfessor ( Vice Director-General, ISM )Kenichiro SHIMATANI

Associate Professor ( Department of Data Science, ISM )Research Ethics Review Committee ( As of April 1, 2018 )Specialist on epidemiology
and social research

Masayuki KANAI

Professor, School of Human Sciences, Senshu University

Specialist on epidemiology
and social research

Keiko SATO

Associate Professor, Kyoto University Hospital, Institute for Advancementof Clinical and Translational Science Department of EBM ResearchSpecialist in the field of ethics
and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Kindergarten Director, Nishikokubunji Nursery School

Research education staff of ISM

Tadahiko MAEDA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Yoo Sung PARK

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Koji KANEFUJI

Professor ( Department of Data Science, ISM )Research education staff of ISM

Ikuko FUNATOGAWA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Shuhei MANO

Associate Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Professor Emeritus ( As of April 1, 2018 )Kameo MATUSITA

Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

Hiroe TSUBAKI

Genshiro KITAGAWA

Nobuhisa KASHIWAGI

Takashi NAKAMURA

Yoshiyasu TAMURA

31

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social science
theories ), and the 3rd Research Dept. ( operations, research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation ofInformation and Systems of the Inter-University Research Institute based on the National University Corporation Law. The Planning Coordination Chief System was abolished and the position of Vice Director-General was
instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Scienceswere instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.

A new office building was constructed in Minato Ward.

The 6th Research Dept. (statistical theories of human behavior) was instituted.
The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section ( hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.
2009

32

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

2017

July
December

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.
Data Science Center for Creative Design and Manufacturing was instituted.
The International Affairs Unit were instituted, and the Gender Equality Unit, which had been within the Planning
Unit, reorganized within APCS.
The Intellectual Property Unit was reorganized as the Industry-Academia Collaboration and Intellectual Property
Unit within APCS.

2018

April

Research Center for Medical and Health Data Science was instituted.
The NIPR/ISM Joint Administration Office was reorganized as the Tachikawa Administration Department of
the Research Organization of Information and Systems ( ROIS ).

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